tbuckley/cabot-search huggingface dataset:
Abstract 2709: Transcriptomic meta-analysis of non-Hodgkin’s B-cell lymphomas reveals reliance on pathways associated with extracellular matrix
Authors: Naomi Rapier-Sharman, Brett E. Pickett
Journal: Cancer Research (2022)

Abstract We report here the results of a transcriptomic meta-analysis of public data for B-cell Non-Hodgkin’s Lymphomas (BCNHL). In 2016, there were 461,000 cases of Non-Hodgkin’s lymphoma worldwide, resulting in 240,000 deaths that year. BCNHLs pose a significant disease burden worldwide, making up 85-90% of BCNHL cases. BCNHL subtypes include Burkitt’s lymphoma, marginal-zone B-cell lymphomas, follicular lymphoma, diffuse large B-cell lymphoma, and mantle cell lymphoma. There is a growing need to understand the mechanisms of BCNHL pathology. The scientific community has spent a great deal of time and effort to identify the hallmarks of cancer. Since cancers of different subtypes can be considered parallel systems, we have performed a meta-analysis including as many BCNHL subtypes as possible to determine common underlying mechanisms. We performed a transcriptomic meta-analysis of publicly available RNA-sequencing data for BCNHL, consisting of 322 relevant samples across seven distinct studies in the NCBI Gene Expression Omnibus (GEO). To our knowledge, no BCNHL meta-analysis of this magnitude has previously been performed. We found ~10,400 significant differentially expressed genes (FDR p-value &amp;lt;= 0.05) and 33 significantly modulated pathways (FDR p-value &amp;lt;= 0.05). We observed potential common mechanisms for BCNHL’s differentially expressed genes and signaling pathways. Our findings include a significant class of proteoglycans not previously associated with lymphomas as well as significant upregulation of extracellular matrix-associated proteins. This meta-analysis offers fresh insights into the inner workings and mechanisms of B-cell lymphomas that could give rise to improved diagnostics and/or therapeutics. Citation Format: Naomi Rapier-Sharman, Brett E. Pickett. Transcriptomic meta-analysis of non-Hodgkin’s B-cell lymphomas reveals reliance on pathways associated with extracellular matrix [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2709.
Abstract 1271: Detection of ALK, ROS1 and RET fusion transcripts in FFPE samples of non-small cell lung cancer patients using a novel RT-PCR based assay
Authors: Marc G. Denis, Audrey Vallée, Christine Sagan, Guillaume Herbreteau, Sandrine Charpentier, Jaya Rajamani, Michael Lee, Ellen Ordinario
Journal: Cancer Research (2022)

Abstract Background and objective: Detection of genomic rearrangements like ALK fusions are mandatory in non-small cell lung cancer (NSCLC) as those alterations can be targeted by an increasing number of drugs. Fluorescence in-situ hybridization (FISH) is the gold standard but multiplexed technologies allow the analysis of several targets simultaneously. We have evaluated a novel RT-PCR based assay for the detection of ALK, ROS1 and RET rearrangement in NSCLC patients in this research study. Materials and Methods: FFPE tissue sections from 309 patients with late stage NSCLC were screened for ALK and ROS1 status using immunohistochemistry (IHC) and confirmed with FISH. Total nucleic acids were extracted from tissue sections using the Maxwell RSC RNA FFPE kit (Promega). Fusions were detected by RT-PCR using a prototype ALK/RET/ROS1 Fusion Panel assay (Roche). A subset of discordant cases were further analyzed by RNA sequencing using the QIAseq Targeted RNAscan Lung Cancer Panel (Qiagen). Results: All 309 samples were tested by RT-PCR with a 0% invalid rate. ALK fusions were detected in 39 samples: 29 were ALK FISH+, 5 were ALK FISH not contributive, 3 were ALK FISH- (less than 15% tumor cells presenting a gene fusion), and 2 were ALK IHC- and not tested by FISH. ROS1 fusions were detected in 12 samples: 9 were ROS1 FISH+, 1 was ROS1 FISH-, and 2 were ROS1 IHC- and not tested by FISH. RET fusions were detected in 3 samples. The remaining 255 samples were RT-PCR-: 5 were ALK FISH+ containing fusions not covered by the RT-PCR assay, 1 was ALK FISH+ containing ALK polysomy, 1 was ROS1 FISH+ containing a fusion not covered by the RT-PCR assay, 1 was ROS1 FISH+ containing ROS1 amplification, 1 was ROS1 FISH+ but ROS1 RNAseq-, and 246 samples were IHC- or IHC+/FISH-. The overall concordance rate between RT-PCR and FISH for ALK and ROS1 gene rearrangements was 91.9%. Although the RT-PCR assay did not detect 5 ALK and 1 ROS1 fusions due to design limitations, it did identify the presence of fusions in 9 tumors that were undetected by FISH (8 ALK and 1 ROS1). Conclusion: Using limited amount of biological material, RT-PCR was able to detect a substantial majority of ALK and ROS1 fusions identified by FISH, as well as fusions that were missed by the initial IHC/FISH screening. This study demonstrates RT-PCR as a feasible approach to detecting fusions in NSCLC. Citation Format: Marc G. Denis, Audrey Vallée, Christine Sagan, Guillaume Herbreteau, Sandrine Charpentier, Jaya Rajamani, Michael Lee, Ellen Ordinario. Detection of ALK, ROS1 and RET fusion transcripts in FFPE samples of non-small cell lung cancer patients using a novel RT-PCR based assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1271.
Predictors of severity in paradoxical psoriasis from biologic therapies: A systematic review
Authors: David O Croitoru, Steph G Brooks, Nikki Nathanielsz, Orli Silverberg, Ioana Nicolau, Aaron M Drucker, Mark Silverberg, Jensen Yeung, James J Limacher, Vincent Piguet
Journal: Journal of the American Academy of Dermatology (2022)
Abstract 5828: Functional role of long non-coding RNA YTHDF3-AS1 in breast cancer
Authors: Alakesh Bera, Surya Radhakrishnan, Eric Russ, Diya Biswas, Madhan Subramanian, Harvey B. Pollard, Hai Hu, Craig D. Shriver, Roopa Biswas, Meera Srivastava
Journal: Cancer Research (2022)

Abstract Breast cancer (BrCa) is the most common form of cancer in women worldwide, impacting nearly 2.1 million women each year. Importantly, current treatment options for metastatic breast cancer fail to elicit an anti-tumor response, leading to a 5-year survival rate below 30%. While most classes of therapy focus on inhibiting or targeting specific proteins, long non-coding RNAs (lncRNAs) offer a unique, relatively untapped, source of potential therapeutic targets. Ongoing experiments are evaluating the regulatory role of the lncRNA YTHDF3-AS1 (ENSG00000270673), which has the potential to develop into a novel therapeutic intervention of BrCa. Through genome-wide analysis, we found that YTHDF3-AS1 expression correlates with essential developmental regulatory genes such as CA3, VPS28, EXOSC4, and TM2D2 and according to pathway analysis is likely linked with the Wnt pathway. Additionally, TCGA data reveals that over 5% of patients with BrCa have an increased copy number of YTHDF3-AS1. Although the role of YTHDF3-AS1 has yet to be explored, YTHDF3 expression was recently shown to be a critical driver of breast cancer metastasis. Of note, YTHDF3-AS1 is upstream of the protein-coding region, but within the promoter region of the YTHDF3 gene. Mechanistically, this allows the lncRNA YTHDF3-AS1 to potentially upregulate the expression of the protein-coding YTHDF3 gene, as two known functions of antisense lncRNAs describe the ability to 1) induce nearby gene expression through binding to the promoter region and 2) stabilize the RNA product of the nearby gene through binding to the 5’UTR. Interestingly, this manner of relationship between antisense (or "divergent") lncRNAs and their protein-coding partner was shown to be highly prevalent in pluripotent stem cells, with the downregulation of both the antisense and protein-coding transcripts following differentiation. Together, our preliminary analysis and previous literature suggest that the lncRNA YTHDF3-AS1 may contribute to the upregulation of YTHDF3, which is a known metastatic promoting protein. Our current study aims to determine the functional role of YTHDF3-AS1 in BrCa oncogenesis, with an emphasis on the mechanistic actions of this lncRNA for the development of future therapeutic approaches. Citation Format: Alakesh Bera, Surya Radhakrishnan, Eric Russ, Diya Biswas, Madhan Subramanian, Harvey B. Pollard, Hai Hu, Craig D. Shriver, Roopa Biswas, Meera Srivastava. Functional role of long non-coding RNA YTHDF3-AS1 in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5828.
Abstract LB513: <i>In vivo</i> efficacy screen of a CSF-1R inhibitor ABSK021 in mouse syngeneic tumor models reveals immune features that may predict its therapeutic response
Authors: Zhixuan Zhu, Juan Peng, Wan Shi, Zhui Chen, Shuqun Yang
Journal: Cancer Research (2022)

Abstract Colony-stimulating factor 1 (CSF-1) and its receptor CSF-1R play critical roles in immune suppressive and tumor promoting characteristics of tumor-associated macrophages (TAM). ABSK021 is a highly potent and selective inhibitor of CSF-1R that is currently under investigation in phase 1b clinical trials of cancer patients. However, it remains to be elucidated what population might be sensitive to CSF-1R inhibitor. Syngeneic mouse models with complete and functional immune system are important for the translation of preclinical immuno-oncology research into clinical settings. Here, we performed an efficacy screen in ten syngeneic models to evaluate the response to CSF-1R inhibition by ASBK021, and profiled the transcriptomic features by RNA-sequencing in each model. In vivo efficacy studies showed that four models were sensitive to CSF-1R inhibition (tumor growth inhibition value (TGI) ≥ 35%) while six models were insensitive (TGI &amp;lt; 35%). Differentially expressed gene (DEG) analysis and gene set enrichment analysis (GSEA) revealed that the MHC class II genes were down-regulated and immune-related pathways had lower enrichment level in the sensitive models, when compared with insensitive models. Further, the result of in silico immune cell deconvolution by CIBERSORT indicated that the activated CD8+ T cells had significantly negative correlation with TGI in this screen. Taken together, these observations suggested the predictive value of certain immune activation features for the sensitivity to the inhibition of CSF-1R signal. Citation Format: Zhixuan Zhu, Juan Peng, Wan Shi, Zhui Chen, Shuqun Yang. In vivo efficacy screen of a CSF-1R inhibitor ABSK021 in mouse syngeneic tumor models reveals immune features that may predict its therapeutic response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB513.
Annexin V‐Modified Platelet‐Biomimetic Nanomedicine for Targeted Therapy of Acute Ischemic Stroke
Authors: Xingping Quan, Yan Han, Pengde Lu, Yuanfu Ding, Qingfu Wang, Yiyang Li, Jianwen Wei, Qiaoxian Huang, Ruibing Wang, Yonghua Zhao
Journal: Advanced Healthcare Materials (2022)

Thromboembolic stroke is typically characterized by the activation of platelets, resulting in thrombus in the cerebral vascular system, leading to high morbidity and mortality globally. Intravenous thrombolysis by tissue plasminogen activator (tPA) administration within 4.5 h from the onset of symptoms is providing a standard therapeutic strategy for ischemic stroke, but this reagent simultaneously shows potential serious adverse effects, e.g., hemorrhagic transformation. Herein, a novel delivery platform based on Annexin V and platelet membrane is developed for tPA (APLT-PA) to enhance targeting efficiency, therapeutic effects, and reduce the risk of intracerebral hemorrhage in acute ischemic stroke. After preparation by extrusion of platelet membrane and subsequent insertion of Annexin V to liposomes, APLT-PA exhibits a high targeting efficiency to activated platelet in vitro and thrombosis site in vivo, due to the binding to phosphatidylserine (PS) and activated platelet membrane proteins. One dose of APLT-PA leads to obvious thrombolysis and significant improvement of neurological function within 7 days in mice with photochemically induced acute ischemic stroke. This study provides a novel, safe platelet-biomimetic nanomedicine for precise thrombolytic treatment of acute ischemic stroke, and offers new theories for the design and exploitation of cell-mimetic nanomedicine for diverse biomedical applications.
Abstract 953: MEK/ERK induces the EMT transcription factor ZEB1 that is regulated by USP10 and promotes colorectal cancer metastasis that can be inhibited by <i>BRAFV600E</i>-directed therapy
Authors: Bo Qin, Lei Sun, Frank A. Sinicrope
Journal: Cancer Research (2022)

Abstract Background: Constitutive MEK-ERK activation induced by BRAFV600E or RAS mutation promotes tumor metastasis in CRC that is mediated by transcription factors regulating epithelial-to-mesenchymal transition (EMT). We determined the role of ZEB1, an EMT master transcription factor, and its potential regulation by the ubiquitin specific peptidase 10 (USP10), in tumor metastasis mediated by MEK-ERK. Materials and Methods: Isogenic RKO [BRAFV600E or wild-type (wt)] and Colo320 (BRAF/KRAS wt/wt) human CRC cells were utilized. Colo320 cells were transfected with mutant KRASG12D. In addition to parental cells, we generated USP10 shRNA expressing RKO and Colo320 cells. Cells were treated with the MEK1/2 inhibitor cobimetinib or BRAF inhibitor encorafenib. Cell migration was analyzed by wound healing and transwell assays. Protein/protein interaction was analyzed by immunoprecipitation. Protein ubiquitination level was determined by ubiquitination assay. In an orthotopic model, lung metastases in mice were generated by tail vein injection of RKO cells. Results: We demonstrate MEK-ERK activation, induced by BRAFV600E, promotes CRC metastasis through inhibition of the USP10-ZEB1 axis. USP10 is shown to interact with ZEB1 and to remove its K27 linkage ubiquitination chain, thus increasing K48 ubiquitination of ZEB1 protein that results in proteosome-mediated degradation of ZEB1. MEK-ERK induced the phosphorylation of USP10 that is shown to attenuate the USP10-ZEB1 interaction and results in ZEB1 stabilization in RKO and Colo320 cell lines. Increased ZEB1 expression promotes tumor cell migration in vitro and metastasis in a murine xenograft model that could be suppressed by cobimetinib or BRAFV600E-directed therapy with encorafenib and cetuximab. Conclusions: Targeting RAF/RAS-driven MEK/ERK signaling can suppress ZEB1 stabilization to inhibit tumor metastasis. Suppression of metastasis occurs by a novel mechanism of ZEB1 stabilization that is regulated by an atypical USP10 deubiquitinase activity. Citation Format: Bo Qin, Lei Sun, Frank A. Sinicrope. MEK/ERK induces the EMT transcription factor ZEB1 that is regulated by USP10 and promotes colorectal cancer metastasis that can be inhibited by BRAFV600E-directed therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 953.
Abstract 6230: Loss of ALK4 function promotes cancer progression by regulating cell surface TGF-β receptor stability
Authors: Manqi Zhang, Jian Chen, Gerard Conrad Blobe
Journal: Cancer Research (2022)

Abstract Pancreatic cancer is a lethal disease. More than 50% of pancreatic cancer patients are diagnosed with metastatic disease with a dismal 3% five-year survival rate. There is clearly an urgent need to identify novel targets to treat pancreatic cancer patients. Transforming growth factor-β (TGF-β) signaling is dysregulated during cancer progression, inhibiting tumorigenesis while promoting metastasis. Approximately half of pancreatic cancer patients have mutations of genes in the TGF-β signaling pathway, including SMAD4, SMAD3, TGFBR1, TFGBR2, ACVR1B, and ACVR2A. ALK4 (Activin receptor-like kinase 4), which is encoded by ACVR1B, is a type I receptor in the TGF-β superfamily. Mutation or copy number loss of ALK4 occurs in 34% of pancreatic cancer patients, while ALK4 expression is significantly down-regulated in many cancers, including pancreatic cancer, with low ALK4 expression associated with poor clinical outcomes. Here we demonstrate that loss of ALK4 increases cell migration and invasion, induces epithelial to mesenchymal transition (EMT), and promotes local and distal metastasis in pancreatic cancer models. Loss of ALK4 unexpectedly increases activation of the canonical TGF-β signaling pathway and significantly induces TGF-β-mediated expression of EMT mediators and effectors. Moreover, ALK4 loss increases the cell surface expression of TβRs (TGFβ receptor type I and II), without changing the receptor gene expression, by increasing N-linked glycosylation of TβRs, potentially by altering Golgi morphology and function. Proteomic analysis of newly synthesized proteins induced by ALK4 loss identified galectin-3 (Gal-3) as upregulated. Gal-3 is known to be upregulated by MGAT5 (Alpha-1,6-Mannosylglycoprotein 6-Beta-N-Acetylglucosaminyltransferase) and cross-links MGAT5-modified N-glycans on TβRs at the cell surface, suppressing TβRs endocytosis. Consistent with this observation, loss of ALK4 increased MGAT5 mRNA expression and induced Gal-3 protein expression, suggesting that ALK4 might regulate TβRs stability by promoting lattice formation with Gal-3 and preventing internalization. Taken together, our results suggest that the loss of ALK4 increases TβR receptor stability through regulating receptor glycosylation, activating TGF-β signaling, promoting the EMT transition and cancer progression. Citation Format: Manqi Zhang, Jian Chen, Gerard Conrad Blobe. Loss of ALK4 function promotes cancer progression by regulating cell surface TGF-β receptor stability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6230.
Abstract 5043: Predicting melanoma staging using targeted RNA sequencing data using machine learning
Authors: Fahad Shabbir Ahmed, Furqan Bin Irfan
Journal: Cancer Research (2022)

Abstract Introduction: Cancer staging can take essential time and expenses away from patients, both of which should be patients’ management. In this current study, we aim to develop a machine learning-based early TNM staging model. Methods: Normalized ribonucleic acid sequencing (RNA-seq) counts data for melanoma patients was extracted from The Cancer Genome Atlas (TCGA). Six different experiments were run to produce machine learning algorithms for nodal metastasis, distant metastasis, combine (nodal or distant) metastasis, and higher vs lower tumor stage (T4/T3 vs T2/T1/Tis). All datasets were split using 80/20 for training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was used to address the imbalanced distribution of the outcome. The algorithm accuracies were determined by a percent of sensitivity, specificity, predictive values (positive and negative: PPV and NPV), and area under the receiver-operator curve (AUROC). Results: The best model for nodal metastasis was a random forest classifier (RF) with targeted gene expression (TE) showed higher sensitivity (98), specificity (100), PPV (100), NPV (94), and AUROC (1.00, 95%CI 0.91-0.99). TE for distant metastasis with RF, showed sensitivity (0), specificity (100), PPV (0), NPV (100), and AUROC (1.00, 95%CI 0.91-1.00). While TE for combined metastasis (nodal or distant) staging algorithm; Nodal or Distant Metastasis (TE) RF, showed sensitivity (98), specificity (100), PPV (100), NPV (99), and AUROC (1.00, 95%CI 0.88-0.98). The tumor staging (DEG and predicting higher stage i.e stage 3 or higher) algorithm; Tumor Staging (TE) RF, showed sensitivity (100), specificity (100), PPV (100), NPV (100), and AUROC (1.00, 95%CI 0.69-0.89) (Table 1). Conclusion: Our machine learning models can predict tumor staging including higher vs lower stage tumor, nodal metastasis, and combined metastasis with high accuracy. However, these results need to be further validated. Table 1. Machine learning models Experiment Sensitivity Specificity PPV NPV AUROC 95%CI Test Set Accuracy N Nodal Metastasis (Pan-Expression) DNN 66.22 16.98 52.69 26.47 0.495 0.25-0.47 54.55 479 Nodal Metastasis (Targeted Expression) DNN 81.13 100.0 100.0 88.10 0.982 0.62-0.83 90.21 479 Nodal Metastasis (Targeted Expression) RF 97.89 100.00 100.00 94 1.00 0.91-0.99 96.74 458 Distant Metastasis (Targeted Expression) RF 0.00 100.00 0.00 100 1.00 0.91-1.00 94.44 450* Nodal or Distant Metastasis (Targeted Expression) RF 97.56 100.00 100.00 98.56 1.00 0.88-0.98 98.91 458 Tumor Staging (Targeted Expression) RF 100.00 100.00 100.00 100.00 1.00 0.69-0.89 100.00 395 *27 cases with distant metastasis Citation Format: Fahad Shabbir Ahmed, Furqan Bin Irfan. Predicting melanoma staging using targeted RNA sequencing data using machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5043.